Evidence map›Paper›PMID 33561918›Full record

ArticleAnimal bioscience2022

Nonlinear mixed models for characterization of growth trajectory of New Zealand rabbits raised in tropical climate.

Vanusa Castro de Sousa, Daniel Biagiotti, José Lindenberg Rocha Sarmento, Luciano Silva Sena, Priscila Alves Barroso, Sued Felipe Lacerda Barjud, Marisa Karen de Sousa Almeida, Natanael Pereira da Silva Santos

Abstract read
In one paragraph

Article in Animal bioscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Vanusa Castro de SousaGraduate Program in Animal Science, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.
Daniel BiagiottiTecnhical College of Bom Jesus, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.
José Lindenberg Rocha SarmentoDepartment of Animal Science, Federal University of Piauí, Teresina, PI 64049-550, Brazil.
Luciano Silva SenaPhD in Animal Science, Federal University of Piauí, Teresina, PI 64049-550, Brazil.
Priscila Alves BarrosoDepartment of Agronomy, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.
Sued Felipe Lacerda BarjudGraduate Program in Animal Science, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.
Marisa Karen de Sousa AlmeidaGraduate Program in Animal Science, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.
Natanael Pereira da Silva SantosDepartment of Agronomy, Federal University of Piauí, Bom Jesus, PI 64900-000, Brazil.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001Research of Piauí
6 · The paper itself

Abstract

objectiveThe identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography.

methodsPhenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piauí state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R2), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects.

resultsThe von Bertalanffy model was the best fitting model for BW according to the highest value of R2 (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R2 (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm2, respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic).

conclusionThe von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

Indexed as

Absolute Growth RateLongitudinal DataModel SelectionOryctolagus cuniculusRandom Effect

Identifiers

PMID33561918
PMCPMC9065789

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